{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Tutorial on large-scale Thompson sampling\n",
        "\n",
        "This demo currently considers four approaches to discrete Thompson sampling on `m` candidates points:\n",
        "\n",
        "1. **Exact sampling with Cholesky:** Computing a Cholesky decomposition of the corresponding `m x m` covariance matrix which reuqires `O(m^3)` computational cost and `O(m^2)` space. This is the standard approach to sampling from a Gaussian process, but the quadratic memory usage and cubic compliexity limits the number of candidate points.\n",
        "\n",
        "2. **Contour integral quadrature (CIQ):** CIQ [1] is a Krylov subspace method combined with a rational approximation that can be used for computing matrix square roots of covariance matrices, which is the main bottleneck when sampling from a Gaussian process. CIQ relies on computing matrix vector multiplications with the exact kernel matrix which requires `O(m^2)` computational complexity and space. Note that the space complexity can be further lowered to `O(m)` by using [KeOps](https://github.com/getkeops/keops), but this is not covered as part of the tutorial.\n",
        "\n",
        "3. **Lanczos:** Rather than using CIQ, we can solve the linear systems `K^(1/2) v = b` using Lanczos and the conjugate gradient (CG) method. This will be faster than CIQ, but will generally produce samples of worse quality. Similarly to CIQ, [KeOps](https://github.com/getkeops/keops) can be used to improve space complexity of Lanczos.\n",
        "\n",
        "4. **Random Fourier features (RFFs):** The RFF kernel was originally proposed in [2] and we use it as implemented in GPyTorch. RFFs are computationally cheap to work with as the computational cost and space are both `O(km)` where `k` is the number of Fourier features.  Note that while Cholesky and CIQ are able to generate exact samples from the GP model, RFFs are an unbiased approximation and the resulting samples often aren't perfectly calibrated.\n",
        "\n",
        "5. **Pathwise Sampling:** Pathwise sampling [3] uses a decomposition of GP posteriors that decouples the posterior into a prior term and a pathwise data update following Matheron's rule. This approach avoids the need for computing the posterior covariance matrices, leading to a fast and accurate scheme for sampling form GP posteriors. \n",
        "\n",
        "\n",
        "[1] [Pleiss, Geoff, et al. \"Fast matrix square roots with applications to Gaussian processes and Bayesian optimization.\", Advances in neural information processing systems (2020)](https://proceedings.neurips.cc/paper/2020/file/fcf55a303b71b84d326fb1d06e332a26-Paper.pdf)\n",
        "\n",
        "[2] [Rahimi, Ali, and Benjamin Recht. \"Random features for large-scale kernel machines.\", Advances in neural information processing systems (2007)](https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf)\n",
        "\n",
        "[3] [J. Wilson, V. Borovitskiy, A. Terenin, P. Mostowsky, and M. Deisenroth. \"Efficiently sampling functions from Gaussian process posteriors.\" International Conference on Machine Learning (2020).](https://proceedings.mlr.press/v119/wilson20a/wilson20a.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Install dependencies if we are running in colab\n",
        "import sys\n",
        "if 'google.colab' in sys.modules:\n",
        "    %pip install botorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[KeOps] Warning : omp.h header is not in the path, disabling OpenMP. To fix this, you can set the environment\n",
            "                  variable OMP_PATH to the location of the header before importing keopscore or pykeops,\n",
            "                  e.g. using os.environ: import os; os.environ['OMP_PATH'] = '/path/to/omp/header'\n",
            "[KeOps] Warning : Cuda libraries were not detected on the system or could not be loaded ; using cpu only mode\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import time\n",
        "from contextlib import ExitStack\n",
        "\n",
        "import gpytorch\n",
        "import gpytorch.settings as gpts\n",
        "import torch\n",
        "from gpytorch.constraints import Interval\n",
        "from gpytorch.distributions import MultivariateNormal\n",
        "from gpytorch.kernels import RBFKernel, RFFKernel, ScaleKernel\n",
        "from gpytorch.likelihoods import GaussianLikelihood\n",
        "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
        "from torch.quasirandom import SobolEngine\n",
        "from torch import Tensor\n",
        "\n",
        "from botorch.fit import fit_gpytorch_mll\n",
        "from botorch.generation import MaxPosteriorSampling\n",
        "from botorch.models import SingleTaskGP\n",
        "from botorch.test_functions import Hartmann\n",
        "from botorch.utils.sampling import draw_sobol_samples\n",
        "from botorch.sampling.pathwise.posterior_samplers import get_matheron_path_model\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "dtype = torch.double\n",
        "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will use 6 dimensional Hartmann test function, which is typically evaluated on the unit hypercube."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "hart6 = Hartmann(dim=6, negate=True).to(device=device, dtype=dtype)\n",
        "dim = hart6.dim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def generate_batch(\n",
        "    X: Tensor,\n",
        "    Y: Tensor,\n",
        "    batch_size: int,\n",
        "    n_candidates: int,\n",
        "    sampler: str,  # \"cholesky\", \"ciq\", \"rff\", \"lanczos\"\n",
        "    seed: int,\n",
        ") -> Tensor:\n",
        "    assert sampler in (\"cholesky\", \"ciq\", \"rff\", \"lanczos\", \"pathwise\")\n",
        "    assert X.min() >= 0.0 and X.max() <= 1.0 and torch.all(torch.isfinite(Y))\n",
        "\n",
        "    if sampler == \"rff\":\n",
        "        base_kernel = RFFKernel(ard_num_dims=X.shape[-1], num_samples=1024)\n",
        "    else:\n",
        "        base_kernel = RBFKernel(ard_num_dims=X.shape[-1])\n",
        "    covar_module = ScaleKernel(base_kernel)\n",
        "\n",
        "    # Fit a GP model\n",
        "    model = SingleTaskGP(train_X=X, train_Y=Y, covar_module=covar_module)\n",
        "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
        "    fit_gpytorch_mll(mll)\n",
        "\n",
        "    # Draw samples on a Sobol sequence\n",
        "    X_cand = draw_sobol_samples(bounds=hart6.bounds, n=n_candidates, q=1, seed=seed).squeeze(-2)\n",
        "\n",
        "    # Thompson sample\n",
        "    with ExitStack() as es:\n",
        "        if sampler == \"cholesky\":\n",
        "            es.enter_context(gpts.max_cholesky_size(float(\"inf\")))\n",
        "        elif sampler == \"ciq\":\n",
        "            es.enter_context(gpts.fast_computations(covar_root_decomposition=True))\n",
        "            es.enter_context(gpts.max_cholesky_size(0))\n",
        "            es.enter_context(gpts.ciq_samples(True))\n",
        "            es.enter_context(\n",
        "                gpts.minres_tolerance(2e-3)\n",
        "            )  # Controls accuracy and runtime\n",
        "            es.enter_context(gpts.num_contour_quadrature(15))\n",
        "        elif sampler == \"lanczos\":\n",
        "            es.enter_context(\n",
        "                gpts.fast_computations(\n",
        "                    covar_root_decomposition=True, log_prob=True, solves=True\n",
        "                )\n",
        "            )\n",
        "            es.enter_context(gpts.max_lanczos_quadrature_iterations(10))\n",
        "            es.enter_context(gpts.max_cholesky_size(0))\n",
        "            es.enter_context(gpts.ciq_samples(False))\n",
        "        elif sampler == \"rff\":\n",
        "            es.enter_context(gpts.fast_computations(covar_root_decomposition=True))\n",
        "        elif sampler == \"pathwise\":\n",
        "            model = get_matheron_path_model(model=model)\n",
        "        es.enter_context(torch.no_grad())\n",
        "\n",
        "        thompson_sampling = MaxPosteriorSampling(model=model, replacement=False)\n",
        "        X_next = thompson_sampling(X_cand, num_samples=batch_size)\n",
        "\n",
        "    return X_next"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "def run_optimization(\n",
        "    sampler: str,\n",
        "    n_candidates: int,\n",
        "    n_init: int,\n",
        "    max_evals: int,\n",
        "    batch_size: int,\n",
        "    seed: int,\n",
        ") -> tuple[Tensor, Tensor]:\n",
        "    X = draw_sobol_samples(bounds=hart6.bounds, n=n_init, q=1, seed=seed).squeeze(-2)\n",
        "    Y = torch.tensor(\n",
        "        [hart6(x) for x in X], dtype=dtype, device=device\n",
        "    ).unsqueeze(-1)\n",
        "    print(f\"{len(X)}) Best value: {Y.max().item():.2e}\")\n",
        "\n",
        "    inner_seed = seed\n",
        "    while len(X) < max_evals:\n",
        "        # Create a batch\n",
        "        start = time.monotonic()\n",
        "        inner_seed += 1\n",
        "        X_next = generate_batch(\n",
        "            X=X,\n",
        "            Y=Y,\n",
        "            batch_size=min(batch_size, max_evals - len(X)),\n",
        "            n_candidates=n_candidates,\n",
        "            seed=inner_seed,\n",
        "            sampler=sampler,\n",
        "        )\n",
        "        end = time.monotonic()\n",
        "        print(f\"Generated batch in {end - start:.1f} seconds\")\n",
        "        Y_next = torch.tensor(\n",
        "            [hart6(x) for x in X_next], dtype=dtype, device=device\n",
        "        ).unsqueeze(-1)\n",
        "\n",
        "        # Append data\n",
        "        X = torch.cat((X, X_next), dim=0)\n",
        "        Y = torch.cat((Y, Y_next), dim=0)\n",
        "\n",
        "        print(f\"{len(X)}) Best value: {Y.max().item():.2e}\")\n",
        "    return X, Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "batch_size = 5\n",
        "n_init = 10\n",
        "max_evals = 50\n",
        "seed = 12345  # To get the same Sobol points\n",
        "N_CAND = 10_000 if not SMOKE_TEST else 10\n",
        "\n",
        "shared_args = {\n",
        "    \"n_candidates\": N_CAND,\n",
        "    \"n_init\": n_init,\n",
        "    \"max_evals\": max_evals,\n",
        "    \"batch_size\": batch_size,\n",
        "    \"seed\": seed,\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Track memory footprint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "%load_ext memory_profiler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Cholesky"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10) Best value: 6.72e-01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/anaconda3/envs/botorch/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated batch in 18.3 seconds\n",
            "15) Best value: 7.73e-01\n",
            "Generated batch in 14.7 seconds\n",
            "20) Best value: 7.73e-01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/anaconda3/envs/botorch/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated batch in 16.4 seconds\n",
            "25) Best value: 7.73e-01\n",
            "Generated batch in 14.1 seconds\n",
            "30) Best value: 1.10e+00\n",
            "Generated batch in 14.2 seconds\n",
            "35) Best value: 2.15e+00\n",
            "Generated batch in 14.8 seconds\n",
            "40) Best value: 2.83e+00\n",
            "Generated batch in 14.6 seconds\n",
            "45) Best value: 2.90e+00\n",
            "Generated batch in 14.3 seconds\n",
            "50) Best value: 2.94e+00\n",
            "peak memory: 6144.33 MiB, increment: 5866.50 MiB\n"
          ]
        }
      ],
      "source": [
        "%memit X_chol, Y_chol = run_optimization(\"cholesky\", **shared_args)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## RFFs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10) Best value: 6.72e-01\n",
            "Generated batch in 2.0 seconds\n",
            "15) Best value: 6.72e-01\n",
            "Generated batch in 2.0 seconds\n",
            "20) Best value: 6.72e-01\n",
            "Generated batch in 2.2 seconds\n",
            "25) Best value: 6.72e-01\n",
            "Generated batch in 2.3 seconds\n",
            "30) Best value: 6.72e-01\n",
            "Generated batch in 2.3 seconds\n",
            "35) Best value: 7.68e-01\n",
            "Generated batch in 2.6 seconds\n",
            "40) Best value: 8.66e-01\n",
            "Generated batch in 2.3 seconds\n",
            "45) Best value: 1.29e+00\n",
            "Generated batch in 2.4 seconds\n",
            "50) Best value: 1.35e+00\n",
            "peak memory: 1801.69 MiB, increment: 1522.14 MiB\n"
          ]
        }
      ],
      "source": [
        "%memit X_rff, Y_rff = run_optimization(\"rff\", **shared_args)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Lanczos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10) Best value: 6.72e-01\n",
            "Generated batch in 2.2 seconds\n",
            "15) Best value: 6.72e-01\n",
            "Generated batch in 2.4 seconds\n",
            "20) Best value: 6.72e-01\n",
            "Generated batch in 2.6 seconds\n",
            "25) Best value: 6.72e-01\n",
            "Generated batch in 2.6 seconds\n",
            "30) Best value: 2.19e+00\n",
            "Generated batch in 2.6 seconds\n",
            "35) Best value: 2.48e+00\n",
            "Generated batch in 2.9 seconds\n",
            "40) Best value: 2.61e+00\n",
            "Generated batch in 3.1 seconds\n",
            "45) Best value: 2.98e+00\n",
            "Generated batch in 3.2 seconds\n",
            "50) Best value: 2.98e+00\n",
            "peak memory: 3264.67 MiB, increment: 1462.98 MiB\n"
          ]
        }
      ],
      "source": [
        "%memit X_lanczos, Y_lanczos = run_optimization(\"lanczos\", **shared_args)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## CIQ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10) Best value: 6.72e-01\n",
            "Generated batch in 11.7 seconds\n",
            "15) Best value: 1.06e+00\n",
            "Generated batch in 14.7 seconds\n",
            "20) Best value: 1.10e+00\n",
            "Generated batch in 11.1 seconds\n",
            "25) Best value: 1.10e+00\n",
            "Generated batch in 14.0 seconds\n",
            "30) Best value: 1.10e+00\n",
            "Generated batch in 17.3 seconds\n",
            "35) Best value: 1.10e+00\n",
            "Generated batch in 14.6 seconds\n",
            "40) Best value: 1.67e+00\n",
            "Generated batch in 15.8 seconds\n",
            "45) Best value: 2.66e+00\n",
            "Generated batch in 16.7 seconds\n",
            "50) Best value: 2.88e+00\n",
            "peak memory: 1907.47 MiB, increment: 1150.44 MiB\n"
          ]
        }
      ],
      "source": [
        "%memit X_ciq, Y_ciq = run_optimization(\"ciq\", **shared_args)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Pathwise Sampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10) Best value: 6.72e-01\n",
            "Generated batch in 0.2 seconds\n",
            "15) Best value: 8.23e-01\n",
            "Generated batch in 0.3 seconds\n",
            "20) Best value: 8.23e-01\n",
            "Generated batch in 0.2 seconds\n",
            "25) Best value: 8.23e-01\n",
            "Generated batch in 0.3 seconds\n",
            "30) Best value: 8.23e-01\n",
            "Generated batch in 0.2 seconds\n",
            "35) Best value: 2.01e+00\n",
            "Generated batch in 0.3 seconds\n",
            "40) Best value: 2.36e+00\n",
            "Generated batch in 0.2 seconds\n",
            "45) Best value: 2.36e+00\n",
            "Generated batch in 0.2 seconds\n",
            "50) Best value: 2.69e+00\n",
            "peak memory: 821.92 MiB, increment: 283.30 MiB\n"
          ]
        }
      ],
      "source": [
        "%memit X_path, Y_path = run_optimization(\"pathwise\", **shared_args)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot\n",
        "\n",
        "Note: These plots are the result of one replication only, and should not be interpreted as one method being better than the others. There will be natural variation in the results if we re-run the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(10, 8))\n",
        "matplotlib.rcParams.update({\"font.size\": 20})\n",
        "\n",
        "results = [\n",
        "    (Y_chol.cpu(), f\"Cholesky-{N_CAND}\", \"b\", \"\", 14, \"--\"),\n",
        "    (Y_rff.cpu(), f\"RFF-{N_CAND}\", \"r\", \".\", 16, \"-\"),\n",
        "    (Y_lanczos.cpu(), f\"Lanczos-{N_CAND}\", \"m\", \"^\", 9, \"-\"),\n",
        "    (Y_ciq.cpu(), f\"CIQ-{N_CAND}\", \"g\", \"*\", 12, \"-\"),\n",
        "    (Y_path.cpu(), f\"Pathwise-{N_CAND}\", \"y\", \">\", 12, \"-.\"),\n",
        "]\n",
        "\n",
        "optimum = hart6.optimal_value\n",
        "\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "names = []\n",
        "for res, name, c, m, ms, ls in results:\n",
        "    names.append(name)\n",
        "    fx = res.cummax(dim=0)[0]\n",
        "    t = 1 + np.arange(len(fx))\n",
        "    plt.plot(t[0::2], fx[0::2], c=c, marker=m, linestyle=ls, markersize=ms)\n",
        "\n",
        "plt.plot([0, max_evals], [hart6.optimal_value, hart6.optimal_value], \"k--\", lw=3)\n",
        "plt.xlabel(\"Function value\", fontsize=18)\n",
        "plt.xlabel(\"Number of evaluations\", fontsize=18)\n",
        "plt.title(\"Hartmann6\", fontsize=24)\n",
        "plt.xlim([0, max_evals])\n",
        "plt.ylim([0, 3.5])\n",
        "\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.legend(\n",
        "    names + [\"Global optimal value\"],\n",
        "    loc=\"lower right\",\n",
        "    ncol=1,\n",
        "    fontsize=18,\n",
        ")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.14"
    }
  },
  "nbformat": 4,
 "nbformat_minor": 4
}
